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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code>.GroupShuffleSplit</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-model-selection-groupshufflesplit">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.model_selection.GroupShuffleSplit</span></code></a></li>
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  <div class="section" id="sklearn-model-selection-groupshufflesplit">
<h1><a class="reference internal" href="../classes.html#module-sklearn.model_selection" title="sklearn.model_selection"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.model_selection</span></code></a>.GroupShuffleSplit<a class="headerlink" href="#sklearn-model-selection-groupshufflesplit" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.model_selection.GroupShuffleSplit">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.model_selection.</code><code class="sig-name descname">GroupShuffleSplit</code><span class="sig-paren">(</span><em class="sig-param">n_splits=5</em>, <em class="sig-param">test_size=None</em>, <em class="sig-param">train_size=None</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/model_selection/_split.py#L1448"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit" title="Permalink to this definition">¶</a></dt>
<dd><p>Shuffle-Group(s)-Out cross-validation iterator</p>
<p>Provides randomized train/test indices to split data according to a
third-party provided group. This group information can be used to encode
arbitrary domain specific stratifications of the samples as integers.</p>
<p>For instance the groups could be the year of collection of the samples
and thus allow for cross-validation against time-based splits.</p>
<p>The difference between LeavePGroupsOut and GroupShuffleSplit is that
the former generates splits using all subsets of size <code class="docutils literal notranslate"><span class="pre">p</span></code> unique groups,
whereas GroupShuffleSplit generates a user-determined number of random
test splits, each with a user-determined fraction of unique groups.</p>
<p>For example, a less computationally intensive alternative to
<code class="docutils literal notranslate"><span class="pre">LeavePGroupsOut(p=10)</span></code> would be
<code class="docutils literal notranslate"><span class="pre">GroupShuffleSplit(test_size=10,</span> <span class="pre">n_splits=100)</span></code>.</p>
<p>Note: The parameters <code class="docutils literal notranslate"><span class="pre">test_size</span></code> and <code class="docutils literal notranslate"><span class="pre">train_size</span></code> refer to groups, and
not to samples, as in ShuffleSplit.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>n_splits</strong><span class="classifier">int (default 5)</span></dt><dd><p>Number of re-shuffling &amp; splitting iterations.</p>
</dd>
<dt><strong>test_size</strong><span class="classifier">float, int, None, optional (default=None)</span></dt><dd><p>If float, should be between 0.0 and 1.0 and represent the proportion
of groups to include in the test split (rounded up). If int,
represents the absolute number of test groups. If None, the value is
set to the complement of the train size. By default, the value is set
to 0.2.
The default will change in version 0.21. It will remain 0.2 only
if <code class="docutils literal notranslate"><span class="pre">train_size</span></code> is unspecified, otherwise it will complement
the specified <code class="docutils literal notranslate"><span class="pre">train_size</span></code>.</p>
</dd>
<dt><strong>train_size</strong><span class="classifier">float, int, or None, default is None</span></dt><dd><p>If float, should be between 0.0 and 1.0 and represent the
proportion of the groups to include in the train split. If
int, represents the absolute number of train groups. If None,
the value is automatically set to the complement of the test size.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, optional (default=None)</span></dt><dd><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <code class="docutils literal notranslate"><span class="pre">np.random</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Examples</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GroupShuffleSplit</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">groups</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">groups</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(8,)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">gss</span> <span class="o">=</span> <span class="n">GroupShuffleSplit</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">train_size</span><span class="o">=.</span><span class="mi">7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">gss</span><span class="o">.</span><span class="n">get_n_splits</span><span class="p">()</span>
<span class="go">2</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">train_idx</span><span class="p">,</span> <span class="n">test_idx</span> <span class="ow">in</span> <span class="n">gss</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">):</span>
<span class="gp">... </span>   <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;TRAIN:&quot;</span><span class="p">,</span> <span class="n">train_idx</span><span class="p">,</span> <span class="s2">&quot;TEST:&quot;</span><span class="p">,</span> <span class="n">test_idx</span><span class="p">)</span>
<span class="go">TRAIN: [2 3 4 5 6 7] TEST: [0 1]</span>
<span class="go">TRAIN: [0 1 5 6 7] TEST: [2 3 4]</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.model_selection.GroupShuffleSplit.get_n_splits" title="sklearn.model_selection.GroupShuffleSplit.get_n_splits"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_n_splits</span></code></a>(self[, X, y, groups])</p></td>
<td><p>Returns the number of splitting iterations in the cross-validator</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.model_selection.GroupShuffleSplit.split" title="sklearn.model_selection.GroupShuffleSplit.split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split</span></code></a>(self, X[, y, groups])</p></td>
<td><p>Generate indices to split data into training and test set.</p></td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="sklearn.model_selection.GroupShuffleSplit.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">n_splits=5</em>, <em class="sig-param">test_size=None</em>, <em class="sig-param">train_size=None</em>, <em class="sig-param">random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/model_selection/_split.py#L1516"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize self.  See help(type(self)) for accurate signature.</p>
</dd></dl>

<dl class="method">
<dt id="sklearn.model_selection.GroupShuffleSplit.get_n_splits">
<code class="sig-name descname">get_n_splits</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X=None</em>, <em class="sig-param">y=None</em>, <em class="sig-param">groups=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/model_selection/_split.py#L1336"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.get_n_splits" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of splitting iterations in the cross-validator</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">object</span></dt><dd><p>Always ignored, exists for compatibility.</p>
</dd>
<dt><strong>y</strong><span class="classifier">object</span></dt><dd><p>Always ignored, exists for compatibility.</p>
</dd>
<dt><strong>groups</strong><span class="classifier">object</span></dt><dd><p>Always ignored, exists for compatibility.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>n_splits</strong><span class="classifier">int</span></dt><dd><p>Returns the number of splitting iterations in the cross-validator.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

<dl class="method">
<dt id="sklearn.model_selection.GroupShuffleSplit.split">
<code class="sig-name descname">split</code><span class="sig-paren">(</span><em class="sig-param">self</em>, <em class="sig-param">X</em>, <em class="sig-param">y=None</em>, <em class="sig-param">groups=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/model_selection/_split.py#L1539"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.model_selection.GroupShuffleSplit.split" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate indices to split data into training and test set.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like, shape (n_samples, n_features)</span></dt><dd><p>Training data, where n_samples is the number of samples
and n_features is the number of features.</p>
</dd>
<dt><strong>y</strong><span class="classifier">array-like, shape (n_samples,), optional</span></dt><dd><p>The target variable for supervised learning problems.</p>
</dd>
<dt><strong>groups</strong><span class="classifier">array-like, with shape (n_samples,)</span></dt><dd><p>Group labels for the samples used while splitting the dataset into
train/test set.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Yields</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>train</strong><span class="classifier">ndarray</span></dt><dd><p>The training set indices for that split.</p>
</dd>
<dt><strong>test</strong><span class="classifier">ndarray</span></dt><dd><p>The testing set indices for that split.</p>
</dd>
</dl>
</dd>
</dl>
<p class="rubric">Notes</p>
<p>Randomized CV splitters may return different results for each call of
split. You can make the results identical by setting <code class="docutils literal notranslate"><span class="pre">random_state</span></code>
to an integer.</p>
</dd></dl>

</dd></dl>

<div class="section" id="examples-using-sklearn-model-selection-groupshufflesplit">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.model_selection.GroupShuffleSplit</span></code><a class="headerlink" href="#examples-using-sklearn-model-selection-groupshufflesplit" title="Permalink to this headline">¶</a></h2>
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